Reinforcement Learning (RL) has made promising progress in planning and decision-
making for Autonomous Vehicles (AVs) in simple driving scenarios. However, existing RL …

Reinforcement learning (RL) has shown promising performance in autonomous driving
applications in recent years. The early end-to-end RL method is usually unexplainable and …

Planning safe trajectories under uncertain and dynamic conditions makes the autonomous
driving problem significantly complex. Current heuristic-based algorithms such as the slot …

Autonomous driving systems (ADSs) have the potential to revolutionize transportation by
improving traffic safety and efficiency. As the core component of ADSs, maneuver decision …

Automated driving in urban settings is challenging. Human participant behavior is difficult to
model, and conventional, rule-based Automated Driving Systems (ADSs) tend to fail when …

Existing deep reinforcement learning (RL) are devoted to research applications on video
games, eg, The Open Racing Car Simulator (TORCS) and Atari games. However, it remains …

In recent years, control under urban intersection scenarios has become an emerging
research topic. In such scenarios, the autonomous vehicle confronts complicated situations …

Reinforcement learning (RL) shows promise for autonomous driving decision-making.
However, designing appropriate reward functions to guide RL agents towards complex …

Urban autonomous driving decision making is challenging due to complex road geometry
and multi-agent interactions. Current decision making methods are mostly manually …

Reinforcement learning (RL) has gained significant interest for its potential to improve
decision and control in autonomous driving. However, current approaches have yet to …